Overview – dataset comparison II Comparison of analyses of the QTLMAS XII common dataset . II : genome-wide association and fine mapping

As part of the QTLMAS XII workshop, a simulated dataset was distributed and participants were invited to submit analyses of the data based on genome-wide association, fine mapping and genomic selection. We have evaluated the findings from the groups that reported fine mapping and genome-wide association (GWA) efforts to map quantitative trait loci (QTL). Generally the power to detect QTL was high and the Type 1 error was low. Estimates of QTL locations were generally very accurate. Some methods were much better than others at estimating QTL effects, and with some the accuracy depended on simulated effect size or minor allele frequency. There were also indications of bias in the effect estimates. No epistasis was simulated, but the two studies that included searches for epistasis reported several interacting loci, indicating a problem with controlling the Type I error rate in these analyses. Although this study is based on a single dataset, it indicates that there is a need to improve fine mapping and GWA methods with respect to estimation of genetic effects, appropriate choice of significance thresholds and analysis of epistasis.

[1]  M. Lund,et al.  Bmc Proceedings Comparison of Analyses of the Qtlmas Xii Common Dataset. I: Genomic Selection , 2022 .

[2]  F. V. van Eeuwijk,et al.  A Bayesian QTL linkage analysis of the common dataset from the 12th QTLMAS workshop , 2009, BMC Proceedings.

[3]  Leif Andersson,et al.  Domestic-animal genomics: deciphering the genetics of complex traits , 2004, Nature Reviews Genetics.

[4]  J. Cheverud.  Genetics and analysis of quantitative traits , 1999 .

[5]  C. Haley,et al.  Genomewide Rapid Association Using Mixed Model and Regression: A Fast and Simple Method For Genomewide Pedigree-Based Quantitative Trait Loci Association Analysis , 2007, Genetics.

[6]  P. Phillips.  Epistasis — the essential role of gene interactions in the structure and evolution of genetic systems , 2008, Nature Reviews Genetics.

[7]  Thomas Mailund,et al.  Whole genome association mapping by incompatibilities and local perfect phylogenies , 2006, BMC Bioinformatics.

[8]  Leonid Kruglyak,et al.  The road to genome-wide association studies , 2008, Nature Reviews Genetics.

[9]  I. Hoeschele,et al.  Advances in statistical methods to map quantitative trait loci in outbred populations. , 1997, Genetics.

[10]  N. Deeb,et al.  Evaluation of a genome-wide approach to multiple marker association considering different marker densities , 2009, BMC proceedings.

[11]  K. Lindblad-Toh,et al.  Efficient mapping of mendelian traits in dogs through genome-wide association , 2007, Nature Genetics.

[12]  François Guillaume,et al.  A strategy for QTL fine-mapping using a dense SNP map , 2009, BMC proceedings.

[13]  M. Pérez-Enciso,et al.  Data modeling as a main source of discrepancies in single and multiple marker association methods , 2009, BMC proceedings.

[14]  Comparison of analyses of the QTLMAS XIV common dataset. I: genomic selection , 2009 .

[15]  M. Lynch,et al.  Genetics and Analysis of Quantitative Traits , 1996 .

[16]  Sigbjørn Lien,et al.  Fine mapping of a quantitative trait locus for twinning rate using combined linkage and linkage disequilibrium mapping. , 2002, Genetics.

[17]  Chris S Haley,et al.  A combined strategy for quantitative trait loci detection by genome-wide association , 2009, BMC proceedings.

[18]  J. Bastiaansen,et al.  Efficient detection of QTL with large effects in a simulated pig-type pedigree using selective genotyping , 2009, BMC proceedings.